• The Journal of Society for e-Business Studies
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• v.18 no.1
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• pp.71-95
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• 2013

This study aims to validate the cost of building the infrastructure to commercialize online electric vehicles. For that purpose, we probe the cost to construct the necessary infrastructure for online electric vehicles regarding Seoul area public bus transit. OLEV and PEV are considered as alternative electric vehicle schemes, and each of them has their own cons and pros in terms of rechargeable battery cost and charger cost. An optimization model which minimizes the cost to install online electric bus feeding devices is proposed in order to compare the total costs of the two alternative schemes. We developed a Mixed Integer Programming model to locate the feeding devices of several different lengths at each bus stops. Furthermore, we implemented a computer simulator to obtain the parameters which will be used in the MIP model and a Web-based system which determines the optimal location of infrastructure for the whole city area from a result of the MIP model. The cost comparison result shows that the total cost of OLEV is cheaper than that of PEV considering the real data of Seoul area public transit, and, as a result, confirms the feasibility of the commercialization of OLEV.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.353-360
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• 2015

The offshore oil production requires a huge amount of cost and time accompanied by multiple variables due to the peculiar nature of 'offshore'. And every process concerned is controlled by elaborate series of plans for reducing loss of lives, environment and property. This paper treats an optimization problem for offshore oil production and transportation. We present an offshore production and transportation network to define scope of the problem and construct a mixed integer linear programming model to tackle it. To demonstrate the validity of the optimization model presented, some computational experiments based on hypothetical offshore oil fields and demand markets are carried out by using MS Office Excel solver. The downstream of the offshore production and transportation network ends up with the maritime transportation problem distributing the crude oil produced from offshore fields to demand markets. We used MoDiSS(Model-based DSS in Ship Scheduling) which was built to resolve this maritime transportation problem. The paper concludes with the remark that the results of the study might be meaningfully applicable to the real world problems of offshore oil production and transportation.

• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.524-530
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• 2019

Purpose: In this paper, we construct a detailed three-dimensional interface element using a three-dimensional analysis program, and evaluate the composite behavior stability of the connector by applying physical properties such as the characteristics of general members and those of reinforced members Method: The analytical model uses solid elements, including non-linear material behavior, to complete the modeling of beam structures, circular flanges, bolting systems, etc. to the same dimensions as the design drawing, with each member assembled into one composite behavior linkage. In order to more effectively control the uniformity and mesh generation of other element type contact surfaces, the partitioning was performed. Modeled with 50 carbon steel materials. Results: It shows the displacement, deformation, and stress state of each load stage by the contact adjoining part, load loading part, fixed end part, and vulnerable anticipated part by member, and after displacement, deformation, The effect of the stress distribution was verified and the validity of the design was verified. Conclusion: Therefore, if the design support of the micro pile is determined based on this result, it is possible to identify the Vulnerable Parts of the composite behavior connector and the degree of reinforcement.

• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Journal of Distribution Science
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• v.14 no.4
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• pp.93-101
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• 2016

Purpose - As more consumers pursue high quality products at reasonable prices, Korean retail companies are increasing investment in expanding their outlet stores. Despite the growing importance of the outlet business, there has been very little empirical research on consumers' outlet shopping behaviors. This study aimed to investigate the relationships between consumers' perceived product value (performance quality, value for money, and social value) of outlet stores and overall shopping satisfaction and the effect of shopping satisfaction on outlet store loyalty. Research design, data and methodology - The authors developed a structural model in which performance quality, value for money, and social value of products are proposed to affect overall outlet shopping satisfaction, thus increasing customer loyalty. To analyze the research model, data were collected from 88 shoppers at suburban outlets. SPSS 21.0 and AMOS 21.0 were utilized to test the hypotheses. The unidimensionality of each construct was supported from the results of the reliability test with Cronbach's α and confirmatory factor analyses. Correlation analysis was performed and the results warranted the nomological validity of the measures. The fit statistics of the overall model analysis demonstrated an acceptable fit(X²(₁₆₁)=171.651, p=.000; X²/df=1.546; GFI=.821, NFI=.879, TLI=.942, CFI=.953, RMR=.035, RMSEA=.079). Results - The findings are as follows. First, consumers' perceived value of product performance quality had a significant positive effect on overall outlet shopping satisfaction. Consumers, who evaluate performance quality of the product more positively, tend to express stronger satisfaction and happiness about outlet shopping experience. Second, consumers' perceived social value of outlet products influenced their overall satisfaction significantly. Consumers who believe that products of outlet stores enhance self-concepts are more likely to satisfy with outlet shopping experience. However, consumers' perception of outlet products on value for money was not found to significantly influence overall shopping satisfaction. Finally, overall shopping satisfaction had a significant and positive influence on loyalty. Conclusions - While outlet retailers have traditionally focused on promoting competitively priced merchandise, the results of this study suggest that customers' overall satisfaction with outlet shopping is influenced more by the non-price-related product values. In the context of an outlet shopping environment, performance quality and social value of the products were found to be more critical predictors of customer overall satisfaction. Therefore, it would not be efficient for outlet retailers to highlight economic value of their merchandise. Instead, they need to investigate the performance quality of the products regularly and try to deliver quality guaranteed goods to enhance customer satisfaction. Also, outlet retailers should differentiate their businesses by carrying more unique and prestigious brands and emphasize higher social value and symbolic meanings of their products. As competition among outlet retailers are getting fierce, retail companies need to focus on strengthening customer loyalty with a long-term perspective. With a deeper understanding of the relationship between consumers' perceived product values and shopping satisfaction, outlet retailers will be able to develop customer loyalty strategies effectively and to achieve competitive advantage.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• Journal of Family Resource Management and Policy Review
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• v.25 no.1
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• pp.35-45
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• 2021

Focusing on increasing of single-person households this study aims to develop a scale to measure the healthiness of lifestyle among single-person households. The concept of healthiness of lifestyle is based on the theories of family strength and family ecology. We draw 50 items that encompass basic needs, individual, familial, and social aspects of single-person life. Using a sample of 317 persons who live alone, this study examined a factor structure of the items and selected 44 items based on the results of factor analysis. Reliability and criterion- and construct validity were also examined. The final scale consists of four domains; basic needs (finance, housing, consumption, and future plan), work·life balance (time management, health, and stress), family relations, and social participation (social network, social interests, and community participation). This scale can be used as an assessment measure of the healthiness of lifestyle of single persons who participate in programs in Healthy and Multicultural Families Support Centers.

• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.825-838
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• 2018

The purpose of this study is to develop a survey tool of scientific character for elementary student which connects science education and character education effectively by figuring out traits of elementary students' character being presented in teaching and learning context of elementary school science. For this, we adapted the theocratical model from the previous research which defined scientific character as the competencies being able to practice in concrete teaching and learning context of science. Based on this model, we developed the survey tool as 'Scientific Character Inventory for Elementary Student' to assess elementary students' scientific character as the competences to practice the virtues being pursued in the context of elementary school science and verified its reliability and validity. As a result of an exploratory and confirmatory factor analysis, we confirmed all the items could be summarized into 28 items and eight constructs such as scientific problem-solving, self-management, self-reflection, communication, interpersonal skill, community participation, global citizenship, and environmental ethics awareness. We found that minimum reliability coefficient of constructs was over than 0.5 and reliability coefficient of the total items was 0.878. And also, there was modest relationship between each construct and the total score of scientific character. These results show that the developed survey tool can be useful in evaluating the effectiveness of science character education. This study is meaningful in that it systematically reveals constructs of scientific character which can be raised in concrete context of science teaching and learning so as to suggest the survey tool to assess this.

• Journal of Family Resource Management and Policy Review
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• v.26 no.2
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• pp.53-70
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• 2022

This study aimed to develop a scale to measure the family strength of single-parent families. We analyzed the everyday life and demands of single-parent families using the theory of family strength to draw 78 items that encompass family basis, relationships, roles, social networks and family culture. Using a sample of 286 single-parent families through an online survey platform, we examined the factor structure of the items and selected 48 items based on the results of the factor analysis. Reliability, criterion and construct validity were also examined. The final scale comprised of five domains ; basis, parents' role, work-life balance, social network, lifestyle and household management. This scale can be used as an assessment measure of the family strength of single-parent families for consulting, case management and suggesting various programs in the field. This merit will help enhance the quality of programing for single-parent families at the Healthy Family Support Center and the development of family strength scales for various types of families.

• Journal of Christian Education in Korea
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• v.72
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• pp.183-203
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• 2022

The purpose of this study is to construct factors of AI education utilization competency. AI education utilization competency is used as basic data for education to enhance the AI education competency of pre-service early childhood teachers. To this end, 7 studies related to competency factors and models were selected by searching for previous studies. Seven preceding studies were analyzed. As a result, 18 competency factors were extracted, including understanding of artificial intelligence. The extracted competency elements were divided into six areas, which are divided into understanding subject knowledge through coding, class preparation, class management, class result feedback, class guidance, and self-development. And 15 factors were constructed. The draft formed through coding was improved through review by three early childhood education experts. Factors improved through expert review were structured by classifying them into knowledge, skills, and attitudes to organize the curriculum. The validity of the structured competency factor was verified through expert Delphi. As a result of the Delphi verification, all factors were converged in the first survey. Through this, 6 competency areas, 11 competency factors, and 19 competency factors were composed of knowledge, 10 skills, and 5 attitudes. The implication is that the competency factors presented as a result of this study can be used as basic data for organizing a curriculum to improve the ability of pre-service early childhood teachers to use artificial intelligence education.